Abstract #3560
Improved estimation of renal perfusion with multiple inversion-time acquisitions in arterial spin labeling
Jeff L Zhang 1 , Christopher C Conlin 1 , Jason Mendes 1 , Niels Oesingmann 2 , and Vivian S Lee 1
1
Department of Radiology, University of Utah,
Salt Lake City, Utah, United States,
2
Siemens
Medical Solutions USA, Inc., New York, United States
Conventional perfusion quantification model for renal
ASL data ignores transit delay from tagging site to
tissue voxels. In this study, we compared a
convolution-based model incorporating transit delay to
the conventional approach using both simulated and human
kidney ASL data. We found that the conventional method
to estimate renal perfusion from ASL data was sensitive
to the selection of inversion time (TI), while by
acquiring signals at multiple TIs and analyzing them
with a convolution-based model, we can estimate renal
perfusion with much lower variability.
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